Future Computing: Grand Challenges & Paradigms

1. System 2020:Research Grand Challenges in Computer Architecture Mary Jane Irwin Penn State University John Shen Intel

2. Eniac What is the next big thing ? Mainframes Mini’s Workstations PC’s ???

3. What are the mega trends ? • Wired  Wireless • Telecommunication • Internet/Computing • Patch-work Wireless  Blanket Wireless • Personal Computer  Mobile Computer  Persistent/Transparent Computer • Embedded vs. High-end  Convergence? • Client vs. Server  Convergence?

4. And anticipated usage models ? • Human-centric: • Intelligent spaces • At home, work, school, leisure, … • Active displays, sensory devices, immersive experiences, … • Personal agents • Feature rich gadgets, real-time information • Highly mobile - roam seamlessly from space to space • Infrastructure-centric: • Traditional server farms and data centers • Very large scale information fusion, storage, analysis • Supporting an enormous number of roaming “servers” • Fabric for supporting human-centric uses • Proactively pushing information to roaming agents • Communication and synchronization between spaces

5. The computing paradigm ala Google

6. The computing paradigm ala Nokia

7. CRA System 2020 Workshop • Logistics • December 4 (pm), 5, 6, and 7 (am), 2005 at the Seascape Resort, Monterey Bay • 55 participants (15 industry, 33 academia, 7 CRA and NSF) • Structure • two “bookend” keynotes: Shekhar Borkar (Intel) on “Microarchitecture Challenges for 2015” and Jim Larus (MSR) on “Software Challenges in the Nanoscale Technologies.” • one “industry” panel: Bob Colwell (Consultant), Chuck Moore (AMD), Ravi Nair (IBM), Justin Rattner (Intel), and Steve Scott (Cray) • rest was brainstorming with afternoon report out plenaries • Managed by CRA, funded by NSF

8. What are the components of a GC? • A “grand” scale problem that will require at least a decade of concentrated research to make substantive progress • that has a measurable outcomes/milestones, • that will excite and engage the computer architecture research community, • and that is deserving of considerable investment by funders because it will materially advance the capabilities and conduct of society.

9. 1W Featherweight Supercomputer • For the goal of 1TOP/W will need up to 10,000X improvement in EPO (energy/op) • 1TOP/W = .001 nJ/op vs. today’s ~10nJ/op • Architects are already engaged • Societal impacts … and funding • Societal impacts are clear and compelling: pervasive intelligent sensors, embedded supercomputing appliances, . . . • Funding investments ?

10. Featherweight Challenges • power/energy reduction issues • dynamic and leakage, HW/SW mode controls, . . . • performance improvement issues • CMPs & SMT, heterogeneous cores, programmable accelerators, eDRAMs, NoCs, . . . • technology concerns (65nm45nm32nm) • ↑ process variation, ↑ transient/permanent faults, advanced packaging (SoC  MCP  3D), . . . • design and fabrication concerns • design time & tools, verification & test, fab costs. . . • programmability . . .

11. Popular Parallel Programming (P3) • Software and architecture support that makes parallel programming easy • If 2X per two year perf. gains continue, will soon have 1000-way chip-level parallelism • Architects are becoming engaged but can’t do the job alone • Need compiler, prog. languages, OS & application developers • Societal impacts … and funding • A necessary enabling technology for future chips (e.g., the 1W Featherweight Supercomputer) • Funding investments ?

12. P3 Challenges • new languages/models/compiler issues • that are correct, efficient, scalable, portable, . . . • that require minimal exposure of the programmer to low-level details • and that support multi-modal parallelism • data-parallel, embarrassingly parallel, irregularly parallel • microarchitecture support issues • lightweight thread/process launch, communication and synchronization, monitoring for reliability and thermal hot spots with dynamic adaptation, . . . • development support • benchmarks, prototyping platforms, tools for debugging, performance tuning, . . .

13. Dependable Systems 1. Self-healing hardware and software systems that you can trust your life on • 2x improvement in mean work-to-failure per generation • while reducing the cost of ownership and vendor costs for liability/repair 2. Architects already engaged but can’t do the job alone • A system stack problem – devices, circuits, archs, languages, OS, applications, dependability analysts 3. Societal impacts … and funding • The s/w problem alone is ~ 0.6% GDP of the US • Funding investments ?

14. Dependable Systems Challenges • Host of hardware reliability problems • Transient – SEU, temperature/process variations, … • Permanent – aging, TDDB, NBTI, EM, … • Software reliability issues • Increasing security issues • Dynamically adapt to system constraints of reliability, security, performance, power • Architects can provide low cost solutions • Workload-aware, selective, fast, adaptive • Bring dependability to h/w-s/w interface • Integrated cross-layer solution from devices to applications

15. New Computing Models • Beyond the stored program architecture • data flow? neural network? • “Expanding the box” for architects • neuroscientists, biologists, chemists, . . . • Societal impacts … and funding • Neuro-prosthetics, telepathy, . . . • Funding investments ?

16. “Brain” Challenges • High risk – but high payoff • Neuroscientists are a long way from unraveling the mysteries of the neocortex • Take partial steps – augment certain brain functions (hearing for the deaf, vision for the blind, mobility for the quadrapeligic), • Take advantage of emerging technologies • Heterogeneous systems: silicon + nanosensors and actuators, emerging nanotechnologies (CNT, QCAs, quantum, . . .)

17. Watch for the final report http://www.cra.org/Activities/grand.challenges/architecture/home.html • And check out the reports from the previous Grand Challenges conferences http://www.cra.org/grand.challenges/